• Journal of the Korean Chemical Society
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• v.59 no.2
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• pp.164-178
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• 2015

The purpose of this study was to design a teaching and learning method conductive to the development of creative thinking skills and investigate its effects. It has been developed integrated mind map with feature of visualizing the divergent thinking to the aspects of Science (S), Technology (T) & Engineering (E), Arts (A), Mathematics (M). Integrated mind map can be divided into four types of STEAM type, STEA type, STEM type, STE type depending on the category of key words in the first branch. And Integrated mind map can be divided into three levels of guided, intermediate, open depending on the teacher's guide degree. And also integrated mind map activities were carried out in the form of group, class share as well as individual. This study was implemented during a semester and students in experiment group experienced individual-integrated mind map activity 10 times, group-integrated mind map activity 10 times, class share-integrated mind map activity 3 times. The results indicated that the experimental group presented statistically meaningful improvement in creative thinking skills (p<.05). And there was a statistically meaningful improvement in fluency, flexibility, originality as a sub-category of creative thinking skills(p <.05). Also creative thinking skills are not affected by the level of cognitive, academic performance, gender (p<.05). In conclusion, it was found that 'integrated mind map activity' improved student's creative thinking skills. There was no interaction effect about creative thinking skills between the teaching strategy and cognitive level, achivement, gender of those students.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.311-324
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• 2021

Recently, ten geosites have been considered in Hwaseong for endorsement as national geoparks, including the Jebudo, Gojeongri Dinosaur Egg Fossils, and Ueumdo geosites. The Jebudo geosite in the southern part of the Seoul metropolitan area has great potential for development as a new geoscience educational site because it has geological, geographical (landscape), and ecological significance. In this study, we described the geological characteristics through field surveys in the Jebudo geosite. We evaluated its potential as a geo-education site based on comparative analysis with other geosites in Hwaseong Geopark. In addition, we reviewed the practical effect of field education at geosites on the essential concepts and critical competence-oriented education emphasized in the current 2015 revised science curriculum. The Jebudo Geosite is geologically diverse, with various metamorphic rocks belonging to the Precambrian Seosan Group, such as quartzite, schist, and phyllite. Various geological structures, such as clastic dikes, faults, joints, foliation, and schistosity have also been recorded. Moreover, coastal geological features have been observed, including depositional landforms (gravel and sand beaches, dunes, and mudflats), sedimentary structures (ripples), erosional landforms (sea cliffs, sea caves, and sea stacks), and sea parting. The Jebudo geosite has considerable value as a new geo-education site with geological and geomorphological distinction from the Gojeongri Dinosaur Egg Fossils and Ueumdo geosites. The Jebudo geosite also has opportunities for geo-education and geo-tourism, such as mudflat experiences and infrastructures, such as coastal trails and viewing points. This geosite can help develop diverse geo-education programs that improve key competencies in the science curriculum, such as critical thinking, inquiry, and problem-solving. Furthermore, by conducting optimized geo-education focused on the characteristics of each geosite, the following can be established: (1) the expansion of learning space from school to geopark, (2) the improvement of understanding of specific content elements and linkage between essential concepts, and (3) the extension of the education scope throughout the earth system. There will be positive impacts on communication, participation, and lifelong learning skills through geopark education.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.77-89
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• 2021

In this study, a method has been proposed to improve the performance of hydraulic property estimation model developed by Jeong et al. (2020). In their study, low-dimensional features of the annual groundwater level (GWL) fluctuation patterns extracted based on a Denoising autoencoder (DAE) was used to develop a regression model for predicting hydraulic properties of an aquifer. However, low-dimensional features of the DAE are highly dependent on the precipitation pattern even if the GWL is monitored at the same location, causing uncertainty in hydraulic property estimation of the regression model. To solve the above problem, a process for generating the GWL fluctuation pattern for conditioning the precipitation is proposed based on a conditional variational autoencoder (CVAE). The CVAE trains a statistical relationship between GWL fluctuation and precipitation pattern. The actual GWL and precipitation data monitored on a total of 71 monitoring stations over 10 years in South Korea was applied to validate the effect of using CVAE. As a result, the trained CVAE model reasonably generated GWL fluctuation pattern with the conditioning of various precipitation patterns for all the monitoring locations. Based on the trained CVAE model, the low-dimensional features of the GWL fluctuation pattern without interference of different precipitation patterns were extracted for all monitoring stations, and they were compared to the features extracted based on the DAE. Consequently, it can be confirmed that the statistical consistency of the features extracted using CVAE is improved compared to DAE. Thus, we conclude that the proposed method may be useful in extracting a more accurate feature of GWL fluctuation pattern affected solely by hydraulic characteristics of the aquifer, which would be followed by the improved performance of the previously developed regression model.

• Korean Journal of Environment and Ecology
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• v.28 no.2
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• pp.235-246
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• 2014

The purpose of this study was to determine the correlation between botanical garden visitors' ecological consciousness and their needs, in order to provide some effective measures to manage them. For this purpose, 3 study points were set up: "botanical garden visitors' ecological consciousness and their needs", "differences of such consciousness depending on their demographic variables" and the "relationship between such consciousness and their needs". To this end, Botanical garden visitors were surveyed for an empirical analysis. The visitors' awareness about ecology was measured with Dunlap's 15-item NEP Inventory, while their needs were analyzed in reference to Maslow's 7-Step Human Desire Ladder. The survey was conducted at Botanical garden for 3 days. As a result, a total of 360 questionnaires were returned. The results of this study can be summarized as follows; First, the visitors' ecological consciousness and their needs were higher than normal level. In terms of their consciousness of ecology, their awareness of the ecological crisis potential and anti-humanism were the highest. In terms of their needs, the aesthetic need was the highest, followed by the cognitive need. On the other hand, the needs for self-achievement and self-esteem were the lowest; except them, the higher the needs were positioned at Maslow's ladder of desire, the more responsive the subjects became. As a result of analyzing the correlation between the subjects' consciousness of ecology and their needs, it was found that the correlation was negative in some sub-areas, while being positive in other sub-areas. After all, the ratio of the sub-areas having a positive correlation was 3 times higher than that of the sub-areas having a negative correlation. Even as for the correlation coefficient values, they were higher in the positive sub-areas, which suggests that the correlation between wetland visitors' ecological consciousness and their needs was positive, although at a lower level, in overall terms. As a result of comparatively analyzing visitors' needs by dividing them into 3 sub-groups depending on the levels of their ecological consciousness, it was found that the higher their consciousness of ecology was, the higher their needs were. Overall, botanical garden visitors' ecological awareness was higher than the normal level, and it was estimated that such awareness would continue to increase. Hence, it could be inferred that their needs, particularly their aesthetic and cognitive ones, would also continue to increase. Accordingly, it is important to manage the wetland landscape making use of its visual resources, while keep providing the visitors with the contents fulfilling their need for knowledge.

• Korean Journal of Psychosomatic Medicine
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• v.11 no.2
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• pp.97-117
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• 2003

Objective: This study is to examine the validity and reliability of Computerized Neurocognitive Function Test among normal children in elementary school. Methods: K-ABC, K-PIC, and Computerized Neurocognitive Function Test were performed to the 120 body of normal children(10 of each male and female) from June, 2002 to January, 2003. Those children had over the average of intelligence and passed the rule out criteria. To verify test-retest reliability for those 30 children who were randomly selected, Computerized Neurocognitive Function Test was carried out again 4 weeks later. Results: As a results of correlation analysis for validity test, four of continues performance tests matched with those on adults. In the memory tests, results presented the same as previous research with a difference between forward test and backward test in short-term memory. In higher cognitive function tests, tests were consist of those with different purpose respectively. After performing factor analysis on 43 variables out of 12 tests, 10 factors were raised and the total percent of variance was 75.5%. The reasons were such as: 'sustained attention, information processing speed, vigilance, verbal learning, allocation of attention and concept formation, flexibility, concept formation, visual learning, short-term memory, and selective attention' in order. In correlation with K-ABC to prepare explanatory criteria, selectively significant correlation(p<.0.5-001) was found in subscale of K-ABC. In the test-retest reliability test, the results reflecting practice effect were found and prominent especially in higher cognitive function tests. However, split-half reliability(r=0.548-0.7726, p<.05) and internal consistency(0.628-0.878, p<.05) of each examined group were significantly high. Conclusion: The performance of Computerized Neurocognitive Function Test in normal children represented differ developmental character than that in adult. And basal information for preparing the explanatory criteria could be acquired by searching for the relation with standardized intelligence test which contains neuropsycological background.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.61-79
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• 2023

This study is a case study of overseas banana farms as a global agricultural startup that has hardly been attempted so far in terms of paradigm shift in the industry, beyond regional limitations. It was researched for the purpose of revealing the success factors of 'global agricultural startup' in terms of business process, entrepreneurship, and management dimensions learned through direct participation and observation at the local level. In order to study global agricultural startups, this study also conducted a comparative analysis of global startups (global startups) and global agricultural startups(global agricultural startups). In fact, the analysis consists of 'definition', 'components', and 'success factors', and we want to confirm the difference between the two concepts that can be distinguished. The case analysis tried to maximize the advantages of 'participatory action research' by directly observing and experiencing banana farms. In the case of banana farm cases, by dividing them into preparation process for farm development and farm development and management process, various variables considered in farm management were explained through the whole process of farm management. Through the process of overcoming and responding to specific failure cases, we tried to secure the reliability and validity of the research, and the case studies related to entrepreneurship, management, and organization analyzed by applying them by subdividing them into theoretical areas belonging to components and management that were theorized in existing preceding studies. This study is almost the first study on the process of creating a local entry business by directly moving the head office overseas rather than entering overseas agriculture as a subsidiary, joint venture or overseas corporation. In particular, it is a unique case that corresponds to agriculture in terms of region(Africa), scale(startup), and industry that have not been introduced so far as a global agricultural startup. In terms of entrepreneurship, it also concretely exemplified how entrepreneurship components such as innovativeness, risk-taking propensity, proactiveness, vision sharing, social contribution, leadership, etc., which have not been attempted so far in agricultural cases, are manifested and effective. The management and cultural aspects also went beyond the argument that only cultural aspects are important in overseas business, and also confirmed individual failure cases and their responses in recruitment, job, wage, retirement, development, organizational structure management, etc. As a result, there is significance and implications of this study in that it provides theoretical confirmation as well as practical and responsive basis for 'entrepreneurship', 'farming management', and 'management' aspects in overseas agricultural startup business operation.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.11-27
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• 2024

This study aimed to investigate the role of scientific empathy in influencing students' productive disciplinary engagement in scientific activities and analyze the key factors of scientific empathy that manifest during this process. Twelve fifth-grade students were divided into three subgroups based on their general empathic abilities. Lessons promoting productive disciplinary engagement, integrating design thinking processes, were conducted. Subgroup discourse analysis during idea generation and prototype stages, two of five problem-solving steps, enabled observation of scientific empathy and practice aspects. The results showed that applying scientific empathy effectively through design thinking facilitated students' productive disciplinary engagement in science. In the idea generation stage, we observed an initial increase followed by a decrease in scientific empathy and practice utterances, while during the prototyping stage, utterance frequency increased, particularly in the later part. However, subgroups with lower empathic abilities displayed decreased discourse frequency in scientific empathy and practice during the prototype stage due to a lack of collaborative communication. Across all empathic ability levels, the students articulated all five key factors of scientific empathy through their utterances in situations involving productive science engagement. In the high empathic ability subgroup, empathic understanding and concern were emphasized, whereas in the low empathic ability subgroup, sensitivity, scientific imagination, and situational interest, factors of empathizing with the research object, were prominent. These results indicate that experiences of scientific empathy with research objects, beyond general empathetic abilities, serve as a distinct and crucial factor in stimulating diverse participation and sustaining students' productive engagement in scientific activities during science classes. By suggesting the potential multidimensional impact of scientific empathy on productive disciplinary engagement, this study contributes to discussions on the theoretical structure and stability of scientific empathy in science education.